CORREX

AGI is general intelligence for everyone. CORREX is specific intelligence for one person.

CORREX is an MCP server that turns every correction you make to Claude into a persistent, injectable memory. Your feedback compounds. Your AI starts thinking like you.

Powered by the Engram engine (surface learning from corrections) and the Ghost engine (autonomous learning from rejection).

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The problem

Every time you correct Claude — "don't do it that way", "be more concise", "show the code first" — that correction vanishes.

Tomorrow, Claude starts over. Same mistakes. Same friction.

CORREX fixes this.

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How it works

You correct Claude
        ↓
CORREX records the correction
        ↓
Engram detects the pattern (across multiple sessions)
        ↓
Pattern is promoted to a Rule
        ↓
Next session: Rule is injected before Claude responds
        ↓
Claude already knows what you want

After weeks of use, your Claude is not the same as anyone else's Claude.

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It actually works

This is a live system. These numbers are from real usage, not a demo.

Memory after real-world usage:

Rules:      152 total  (121 promoted, 31 still learning)
Laws:        76 autonomous principles (Ghost-sublimated)
Ghosts:    1588 rejected proposals tracked
Turns:      128 recorded corrections
Meanings:    32 cross-scope patterns extracted
Policies:    10 deep behavioral policies
Journeys:     8 episodic search memories

Sample promoted rules (extracted from real corrections):

[proposal_summary]  提案書要約では顧客の具体的な業務名を必ず含める
                    evidence=2  confidence=0.71

[architecture]      目的関数を固定値として扱うな。ユーザーの状態でゴールポストが動く。
                    evidence=1  confidence=0.60

[coding]            テストが通らないコードをコミットするな
                    evidence=3  confidence=0.88

Measured quality improvement (before vs. after rule injection):

Task	Baseline	With rules	Delta
Proposal summary	0.30	0.85	+55%
LLM A/B simulation	0.37	0.55	+18%
Commercialization proposal	0.62	0.75	+13%

Measurement method: record_growth(case_id, baseline_output, baseline_score, guided_output, guided_score). Scores are user-assigned 0.0–1.0 ratings of output quality on the same task with vs. without build_guidance_context injection. Sample size is small (single user, N=3 cases above). Reproduction: see tests/test_service.py::test_record_growth_*.

Test suite:

$ python -m pytest tests/ -q --ignore=tests/test_memory_manager.py
127 passed, 2 skipped in 1.82s

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The Engram engine — surface learning

Engram is the visible memory layer. It works in 5 sub-layers:

Layer	Signal	What it does
Surface (corrections)	User says "wrong"	Extracts rules, promotes to meanings/principles
Ghost (rejection)	User rejects AI proposal	Clusters rejections, autonomously extracts laws
Curiosity (questions)	User asks questions	Tracks knowledge gaps, warns before frustration
Journey (exploration)	AI visits URLs/files	Episodic memory with dormancy/awakening
Autonomous (self-reflection)	Engine tick	Cross-layer modulation, predictions, self-overcome

Rules promote only when they appear consistently across sessions. Low-confidence rules are automatically demoted. Contradicting rules are auto-resolved.

Engram also builds a personality profile (6 dimensions):

• metabolism_rate — how fast you change your mind
• reward_pattern — what makes you say "yes"
• avoidance_pattern — what makes you say "wrong"
• digestibility — abstract vs. concrete preference
• curiosity_level — how often you ask exploratory questions
• objective_drift — whether your goals have shifted

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The Ghost engine — autonomous learning from rejection

Most memory systems treat rejection as noise to filter out. CORREX treats rejection as the highest-quality learning signal.

When you tell Claude "wrong", "no", "redo", "stop" — CORREX records the rejected proposal as a Ghost. Related Ghosts cluster into trajectories. When a trajectory's cumulative prediction error crosses a threshold, it fires — and an autonomous principle is born without further human input.

Property	Value
Origin types tracked	rejected, corrected, scolded
Cumulative prediction error threshold	1.0 (configurable)
Auto-sublimation	Principle generated when threshold crossed
Confidence gating	Single-noise Ghosts blocked from auto-firing
Storage	~/.correx/ghosts.json + ~/.correx/ghost_trajectories.json

Examples of principles that emerged this way (from real usage):

答えを持っていないなら虚勢をやめろ           (2 trajectories converged)
GateGuard を迂回しないように                 (multi-trajectory)
検出漏れは修正するな                        (2 trajectories converged)
コーディング作業中は避けるな                 (2 trajectories converged)
親子関係は避けること                        (2 trajectories converged)
アンケートは不要とするな                     (2 trajectories converged)

These weren't hand-written. They emerged from rejections, autonomously, and now feed back into Claude's context on every new task.

Why this matters: other memory systems (Mem0, Letta, OpenMemory MCP) record what you say. CORREX additionally records what you reject — and rejection is where your real preferences live.

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Quickstart

Install

git clone https://github.com/ozoz5/correx
cd correx
python -m venv .venv
source .venv/bin/activate
pip install -e ".[mcp]"

Add to Claude Code

claude mcp add correx \
  -s user \
  -- /path/to/correx/.venv/bin/python \
  -m correx \
  --memory-dir ~/.correx \
  --transport stdio

Add to Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "correx": {
      "command": "/path/to/correx/.venv/bin/python",
      "args": [
        "-m", "correx",
        "--memory-dir", "/Users/you/.correx",
        "--transport", "stdio"
      ]
    }
  }
}

Restart Claude. CORREX tools appear automatically.

Enable the auto-learning loop

Copy the template into your project's CLAUDE.md:

cat /path/to/correx/CLAUDE_TEMPLATE.md >> .claude/CLAUDE.md

This instructs Claude to automatically call CORREX tools when you correct it, ask questions, or complete tasks.

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The core loop

# At the start of any task
build_guidance_context(task_title="Write a proposal", task_scope="proposal")

# After Claude responds and you correct it
save_conversation_turn(
    task_scope="proposal",
    user_message="Write the summary",
    assistant_message="Here is a dense summary...",
    user_feedback="Too abstract. Use the client's actual business name.",
    extracted_corrections=["Always include the client's business name in summaries"],
    guidance_applied=True,
)

# Rules promote automatically. Next time: Claude already knows.

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MCP tools

Tool	What it does
build_guidance_context	Injects your accumulated rules into Claude's context
save_conversation_turn	Records a correction or approval
rebuild_preference_rules	Re-scans full history and re-promotes rules
synthesize_meanings	Extracts deeper patterns from rule clusters
synthesize_principles	Distills principles from meanings
get_personality_profile	Shows behavioral profile + self-critique proposals
synthesize_rules	Generates rule hypotheses from success/failure patterns
evaluate_guidance_effectiveness	Self-assessment of which rules helped on the last task
record_growth	Measures before/after quality improvement
save_curiosity_signal	Records a user question (classified by client LLM)
get_cognitive_map	Shows knowledge gap map by scope
save_ghost	Records a rejected AI proposal for autonomous learning
get_ghost_principles	Returns autonomously extracted principles
list_ghost_trajectories	Lists rejection clusters with firing status

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Dashboard

A visual dashboard for monitoring your AI's growth is available as a separate project.

Features:

• System Overview — knowledge hierarchy with live stats
• Policies / Principles / Rules — full knowledge hierarchy visualization
• Growth — before/after quality measurements
• Ghost Trajectories — rejection pattern analysis
• Journey Memory — episodic search traces
• Autonomous Engine — cross-layer modulation view
• Tamagotchi — a pixel art creature that evolves with your AI's personality

Screenshots & GIF demos: coming. The plant grows slowly. Like your AI.

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Architecture

correx/
  src/correx/
    mcp_server.py          # MCP tool definitions (40+ tools)
    service.py             # Core service layer
    history_store.py       # Persistence + atomic I/O
    rule_builder.py        # Pure rule construction logic (no I/O)
    meaning_synthesis.py   # Engram: rules → meanings → principles
    personality_layer.py   # Behavioral profiling (6 dimensions)
    ghost_engine.py        # Rejection → trajectory → autonomous law
    autonomous.py          # Cross-layer intelligence engine (~900 lines)
    dormancy.py            # Dormancy / awakening / forgetting
    text_similarity.py     # Bigram similarity for deduplication
    analytics.py           # Growth & engagement analytics
  tests/                   # 127 tests passing (2 skipped)

All data stored as JSON in ~/.correx/. No database required.

CI note: tests/test_memory_manager.py is currently --ignored in CI because it covers a deprecated memory backend that's being phased out. Active engine code (service.py, ghost_engine.py, autonomous.py, etc.) is fully covered by the remaining 127 tests.

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Optional: LoRA training

CORREX can export your correction history as training data and trigger LoRA fine-tuning on Apple Silicon.

pip install -e ".[train]"

python3 scripts/auto_train.py \
  --model mlx-community/Qwen2.5-1.5B-Instruct-4bit \
  --memory-dir ~/.correx \
  --output-dir ./training_artifacts

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Philosophy

Most AI personalization is about data — what the AI knows about you. CORREX is about behavior — how the AI thinks with you.

The difference:

• Knowing you're vegetarian
• vs. knowing you hate when someone buries the conclusion

AGI is general intelligence for everyone. CORREX is specific intelligence for one person.

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Status

• ✅ Local-first beta (single-user, JSON storage)
• ✅ 127 tests passing (2 skipped)
• ✅ 5-layer Engram engine (Surface → Ghost → Curiosity → Journey → Autonomous)
• ✅ Ghost engine with autonomous principle sublimation
• ✅ Policy synthesis pipeline (corrections → rules → meanings → principles → policies)
• ✅ Dashboard with real-time visualization
• 🔄 npm/pip distribution
• 🔄 Screenshots & GIF demos

Maturity: this is a single-user production prototype. The author runs it daily. It is not yet hardened for multi-user / multi-tenant deployments.

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License

MIT — Copyright (c) 2026 Hirokazu Seto